Chuhan Zhao^1,*, Souad Morsli², Laurent Caramelle³, Mohammed El Ganaoui³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.8, pp. 2001-2025, 2025, DOI:10.32604/fdmp.2025.068093 - 12 September 2025

Abstract Carbon dioxide (CO₂) is often monitored as a convenient yardstick for indoor air safety, yet its ability to stand in for pathogen-laden aerosols has never been settled. To probe the question, we reproduced an open-plan office at full scale (7.2 m 5.2 m 2.8 m) and introduced a breathing plume that carried 4% CO₂, together with a polydisperse aerosol spanning 0.5–10 m (1320 particles s⁻¹). Inlet air was supplied at 0.7, 1.4, and 2.1 m s⁻¹, and the resulting fields were simulated with a Realisable – RANS model coupled to Lagrangian particle tracking. Nine strategically placed probes… More >

Almustafa Abd Elkader Ayek¹, Bilel Zerouali^2,*, Ankur Srivastava³, Mohannad Ali Loho^4,5, Nadjem Bailek^6,7, Celso Augusto Guimarães Santos^8,9

Revue Internationale de Géomatique, Vol.34, pp. 669-689, 2025, DOI:10.32604/rig.2025.067137 - 19 August 2025

Abstract This study investigates the spatial and temporal dynamics of key air pollutants—nitrogen dioxide (NO₂), carbon monoxide (CO), methane (CH₄), formaldehyde (HCHO), and the ultraviolet aerosol index (UVAI)—over the period 2019–2024. Utilizing high-resolution remote sensing data from the Sentinel-5 Precursor satellite and its TROPOspheric Monitoring Instrument (TROPOMI) processed via Google Earth Engine (GEE), pollutant concentrations were analyzed, with spatial visualizations produced using ArcGIS Pro. The results reveal that urban and industrial hotspots—notably in Damascus, Aleppo, Homs, and Hama—exhibit elevated NO₂ and CO levels, strongly correlated with population density, traffic, and industrial emissions. Temporal trends indicate significant pollutant fluctuations More > Graphic Abstract

Yu Zhou^1,*, Shen Ji^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.1, pp. 189-200, 2021, DOI:10.32604/fdmp.2021.011744 - 09 February 2021

Abstract The outbreak of Corona Virus (COVID-19) has spread over a large number of countries. The virus is typically transported inside liquid droplets produced by human beings. As a result, doctors operating in the consulting rooms of hospitals are potentially exposed to high risk. Taking into account the evaporation of droplets and using a hybrid Eulerian-Lagrangian framework to determine the airflow pattern and corresponding motion of droplets, in the present study, the motion of droplets is investigated with regard to the situation in which doctors check patients in front of their bed. A turbulence model (RNG k-ɛ) More >

Ning Li^1,2, Weiming Yi^1,2, Zhihe Li^1,2,*, Lihong Wang^1,2, Yongjun Li^1,2, Xueyuan Bai^1,2, Mei Jiang^1,2

Journal of Renewable Materials, Vol.8, No.3, pp. 329-346, 2020, DOI:10.32604/jrm.2020.08618 - 01 March 2020

Abstract Bio-oil production via pyrolysis is one of promising technologies for renewable energy production from bio-wastes. However, the complicated biooil is still a challenge for high-valued application and during biomass pyrolysis, the emission of non-cleaned aerosol, the potential emission, namely carbonaceous aerosol (CA) increased the difficulty of the commercial promotion. In this study, Rice husk pyrolysis was performed in a semi-continuous fluidized bed reactor coupled with fractional condensers. The effects of pyrolysis and condensation temperature on the properties of bio-oil and emission of CA were investigated systemically. Results indicated that the in-situ separation of vapors was… More >

Qiang Liu^1,*, Yanyun Zou^2,3, Xiaodong Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM_2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM_2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM_2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN)… More >